Building a Smart Python-to-R Code Converter with Gemini AI-Powered Validation and Feedback
AI

Building a Smart Python-to-R Code Converter with Gemini AI-Powered Validation and Feedback

class EnhancedPythonToRConverter: “”” Enhanced Python to R converter with Gemini AI validation “”” def __init__(self, gemini_api_key: str = None): self.validator = GeminiValidator(gemini_api_key) self.import_mappings = { ‘pandas’: ‘library(dplyr)\nlibrary(tidyr)\nlibrary(readr)’, ‘numpy’: ‘library(base)’, ‘matplotlib.pyplot’: ‘library(ggplot2)’, ‘seaborn’: ‘library(ggplot2)\nlibrary(RColorBrewer)’, ‘scipy.stats’: ‘library(stats)’, ‘sklearn’: ‘library(caret)\nlibrary(randomForest)\nlibrary(e1071)’, ‘statsmodels’: ‘library(stats)\nlibrary(lmtest)’, ‘plotly’: ‘library(plotly)’, } self.function_mappings = { ‘pd.DataFrame’: ‘data.frame’, ‘pd.read_csv’: ‘read.csv’, ‘pd.read_excel’: ‘read_excel’, ‘df.head’: ‘head’, ‘df.tail’: ‘tail’,

Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings
AI

Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings

[Submitted on 21 Feb 2024 (v1), last revised 18 Jul 2025 (this version, v2)] View a PDF of the paper titled SecurePose: Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings, by Rishabh Bajpai and Bhooma Aravamuthan View PDF Abstract:Movement disorder diagnosis often relies on expert evaluation of patient videos,

Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings
AI

[2412.20383] Progressively Exploring and Exploiting Cost-Free Data to Break Fine-Grained Classification Barriers

[Submitted on 29 Dec 2024 (v1), last revised 18 Jul 2025 (this version, v2)] View a PDF of the paper titled Progressively Exploring and Exploiting Cost-Free Data to Break Fine-Grained Classification Barriers, by Li-Jun Zhao and 4 other authors View PDF HTML (experimental) Abstract:Current fine-grained classification research primarily focuses on fine-grained feature learning. However, in

Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings
AI

A Survey of Long Chain-of-Thought for Reasoning Large Language Models

[Submitted on 12 Mar 2025 (v1), last revised 18 Jul 2025 (this version, v5)] View a PDF of the paper titled Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models, by Qiguang Chen and 9 other authors View PDF HTML (experimental) Abstract:Recent advancements in reasoning with large language models (RLLMs), such

Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings
AI

Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography

[Submitted on 18 Jul 2025] View a PDF of the paper titled UGPL: Uncertainty-Guided Progressive Learning for Evidence-Based Classification in Computed Tomography, by Shravan Venkatraman and 3 other authors View PDF HTML (experimental) Abstract:Accurate classification of computed tomography (CT) images is essential for diagnosis and treatment planning, but existing methods often struggle with the subtle

Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings
AI

BreastSegNet: Multi-label Segmentation of Breast MRI

arXiv:2507.13604v1 Announce Type: cross Abstract: Breast MRI provides high-resolution imaging critical for breast cancer screening and preoperative staging. However, existing segmentation methods for breast MRI remain limited in scope, often focusing on only a few anatomical structures, such as fibroglandular tissue or tumors, and do not cover the full range of tissues seen in scans.

Automated Face Blurring and Human Movement Kinematics Extraction from Videos Recorded in Clinical Settings
AI

[2409.04617] Sparse Rewards Can Self-Train Dialogue Agents

[Submitted on 6 Sep 2024 (v1), last revised 18 Jul 2025 (this version, v3)] View a PDF of the paper titled Sparse Rewards Can Self-Train Dialogue Agents, by Barrett Martin Lattimer and 3 other authors View PDF Abstract:Recent advancements in state-of-the-art (SOTA) Large Language Model (LLM) agents, especially in multi-turn dialogue tasks, have been primarily

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